Modernizing data science lifecycle management with AWS and Wipro

This article discusses how Wipro, an AWS Premier Tier Services Partner, helped one of its customers modernize their data science and machine learning lifecycle management using AWS services, particularly Amazon SageMaker.

Specifically, the article covers:

• The challenges the customer faced with their current on-premises and open-source setup, including lack of collaboration, scalability issues, lack of integrated MLOps, and lack of reusability.
• The solution architecture implemented by Wipro, which included components like SageMaker notebooks for code sharing, automated CI/CD pipelines, MLOps pipelines for model training, deployment, batch scoring, real-time inference, and custom model monitoring.
• How various AWS services like SageMaker, Lambda, Step Functions, EventBridge, and QuickSight were used to build a scalable, automated, and integrated MLOps framework.
• The benefits achieved, such as streamlined model development, automated retraining, continuous monitoring, and drift analysis, enabling the customer to migrate and develop more models on AWS efficiently.
• Conclusion and a high-level overview of the steps to create a similar architecture.